TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Nature of protein family signatures: Insights from singular value analysis of position-specific scoring matrices

Position-specific scoring matrices (PSSMs) are useful for detecting weak homology in protein sequence analysis, and they are thought to contain some essential signatures of the protein families. In order to elucidate what kind of ingredients constitute such family-specific signatures, we apply singular value decomposition to a set of PSSMs and examine the properties of dominant right and left singular vectors. The first right singular vectors were correlated with various amino acid indices including relative mutability, amino acid composition in protein interior, hydropathy, or turn propensity, depending on proteins. A significant correlation between the first left singular vector and a measure of site conservation was observed. It is shown that the contribution of the first singular component to the PSSMs act to disfavor potentially but falsely functionally important residues at conserved sites. The second right singular vectors were highly correlated with hydrophobicity scales, and the corresponding left singular vectors with contact numbers of protein structures. It is suggested that sequence alignment with a PSSM is essentially equivalent to threading supplemented with functional information. The presented method may be used to separate functionally important sites from structurally important ones, and thus it may be a useful tool for predicting protein functions.Comment: 22 pages, 7 figures, 4 table

An Integrative Multi-Network and Multi-Classifier Approach to Predict Genetic Interactions

Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date

Expression of Trichoderma reesei cellulases CBHI and EGI in Ashbya gossypii

To explore the potential of Ashbya gossypii as a host for the expression of recombinant proteins and to assess whether protein secretion would be more similar to the closely related Saccharomyces cerevisiae or to other filamentous fungi, endoglucanase I (EGI) and cellobiohydrolase I (CBHI) from the fungus Trichoderma reesei were successfully expressed in A. gossypii from plasmids containing the two micron sequences from S. cerevisiae, under the S. cerevisiae PGK1 promoter. The native signal sequences of EGI and CBHI were able to direct the secretion of EGI and CBHI into the culture medium in A. gossypii. Although CBHI activity was not detected using 4- methylumbelliferyl-β-D-lactoside as substrate, the protein was detected by Western blot using monoclonal antibodies. EGI activity was detectable, the specific activity being comparable to that produced by a similar EGI producing S. cerevisiae construct. More EGI was secreted than CBHI, or more active protein was produced. Partial characterization of CBHI and EGI expressed in A. gossypii revealed overglycosylation when compared with the native T. reesei proteins, but the glycosylation was less extensive than on cellulases expressed in S. cerevisiae.Fundação para a Ciência e a Tecnologia (FCT

Increased Anxiety-Like Behavior and Enhanced Synaptic Efficacy in the Amygdala of GluR5 Knockout Mice

GABAergic transmission in the amygdala modulates the expression of anxiety. Understanding the interplay between GABAergic transmission and excitatory circuits in the amygdala is, therefore, critical for understanding the neurobiological basis of anxiety. Here, we used a multi-disciplinary approach to demonstrate that GluR5-containing kainate receptors regulate local inhibitory circuits, modulate the excitatory transmission from the basolateral amygdala to the central amygdala, and control behavioral anxiety. Genetic deletion of GluR5 or local injection of a GluR5 antagonist into the basolateral amygdala increases anxiety-like behavior. Activation of GluR5 selectively depolarized inhibitory neurons, thereby increasing GABA release and contributing to tonic GABA current in the basolateral amygdala. The enhanced GABAergic transmission leads to reduced excitatory inputs in the central amygdala. Our results suggest that GluR5 is a key regulator of inhibitory circuits in the amygdala and highlight the potential use of GluR5-specific drugs in the treatment of pathological anxiety

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Analysis of the tunicamycin biosynthetic gene cluster of Streptomyces chartreusis reveals new insights into tunicamycin production and immunity

The tunicamycin biosynthetic gene cluster of Streptomyces chartreusis consists of 14 genes (tunA-N) with a high degree of apparent translational coupling. Transcriptional analysis revealed that all of these genes are likely to be transcribed as a single operon from two promoters, tunp1 and tunp2. In frame deletion analysis revealed that just six of these genes (tunABCDEH) are essential for tunicamycin production in the heterologous host Streptomyces coelicolor, while five (tunFGKLN) with likely counterparts in primary metabolism are not necessary, but presumably ensure efficient production of the antibiotic at the onset of tunicamycin biosynthesis. Three genes are implicated in immunity; tunIJ, which encode a two component ABC transporter presumably required for export of the antibiotic, and tunM, which encodes a putative SAM-dependent methyltransferase. Expression of tunIJ or tunM in S. coelicolor conferred resistance to exogenous tunicamycin. The results presented here provide new insights into tunicamycin biosynthesis and immunity